$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences
Machine Learning
2024-10-30 v2 Machine Learning
Image and Video Processing
Abstract
We state theoretical properties for -means clustering of Symmetric Positive Definite (SPD) matrices, in a non-Euclidean space, that provides a natural and favourable representation of these data. We then provide a novel application for this method, to time-series clustering of pixels in a sequence of Synthetic Aperture Radar images, via their finite-lag autocovariance matrices.
Keywords
Cite
@article{arxiv.2008.03454,
title = {$k$-means on Positive Definite Matrices, and an Application to Clustering in Radar Image Sequences},
author = {Daniel Fryer and Hien Nguyen and Pascal Castellazzi},
journal= {arXiv preprint arXiv:2008.03454},
year = {2024}
}
Comments
This work has been submitted to the IEEE for possible publication